Skripsi

DETEKSI SERANGAN DDOS DOS DAN MITM PADA PERANGKAT SMART HOME MENGGUNAKAN METODE NAÏVE BAYES

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Penilaian

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This study aims to detect and classify Distributed Denial of Service (DDoS), Denial of Service (DoS), and Man in The Middle (MITM) attacks on smart home devices using the Naïve Bayes method. The research begins by identifying important features of network traffic such as frame.time.epoch, ip.src, ip.dst, eth.src, eth.dst, tcp.srcport, tcp.dstport, arp, and frame.len, which play a crucial role in the attack classification process. The Naïve Bayes method is applied in two variants, Gaussian and Bernoulli, to analyze their effectiveness in classifying attack data. Evaluation is conducted using accuracy, precision, recall, and F1-score metrics. The results show that Gaussian Naïve Bayes performs better than Bernoulli Naïve Bayes, achieving 99.41% accuracy and 95.67% effectiveness, while Bernoulli Naïve Bayes reaches 96.79% accuracy and 91.72% effectiveness. Overall, the Naïve Bayes method proves to be effective and efficient in detecting and classifying various types of cyberattacks in smart home environments, with the Gaussian variant being the most optimal model.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507006249	T185628	T1856282025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1856282025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiii, 98 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

005.840 7

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Prodi Sistem Komputer
Serangan Malware Komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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